This blog in the series on Scalable Vector Search summarizes insights from our study on optimizing vector search settings in RAG systems and offers actionable guidelines for improving RAG pipeline efficiency and effectiveness.
-
-
Articles récents
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
- Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
- AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
- In-production AI Optimization Guide for Xeon: Search and Recommendation Use Case
- Argonne’s Aurora Supercomputer Helps Power Breakthrough Simulations of Quantum Materials
-
Neural networks news
Intel NN News
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
Intel® Xeon® processors can deliver a CPU-first platform built for modern AI workloads without […]
- Document Summarization: Transforming Enterprise Content with Intel® AI for Enterprise RAG
Transform enterprise documents into insights with Document Summarization, optimized for Intel® […]
- AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound
We are thrilled to announce an official collaboration between SGLang and AutoRound, enabling […]
- Next-Gen AI Inference: Intel® Xeon® Processors Power Vision, NLP, and Recommender Workloads
-